Update app.py

app.py

@@ -1,9 +1,46 @@
 import os
 from huggingface_hub import login
+import torch
+import torchaudio
+from einops import rearrange
+import gradio as gr
+from stable_audio_tools import get_pretrained_model
+from stable_audio_tools.inference.generation import generate_diffusion_cond
 
+# Authenticate using token from Secrets
 token = os.getenv("HUGGINGFACE_TOKEN")
+if not token:
+    raise RuntimeError("HUGGINGFACE_TOKEN not set")
+login(token=token, add_to_git_credential=False)
 
-if token is None or token == "":
-    raise RuntimeError("HUGGINGFACE_TOKEN is not set or is empty.")
+# Set device + load model
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model, config = get_pretrained_model("stabilityai/stable-audio-open-small")
+model = model.to(device)
+sample_rate = config["sample_rate"]
+sample_size = config["sample_size"]
 
-login(token=token, add_to_git_credential=False)
+# Inference function
+def generate_audio(prompt):
+    conditioning = [{"prompt": prompt, "seconds_total": 11}]
+    with torch.no_grad():
+        output = generate_diffusion_cond(
+            model,
+            steps=8,
+            conditioning=conditioning,
+            sample_size=sample_size,
+            device=device
+        )
+    output = rearrange(output, "b d n -> d (b n)")
+    output = output.to(torch.float32).div(torch.max(torch.abs(output))).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
+    path = "output.wav"
+    torchaudio.save(path, output, sample_rate)
+    return path
+
+# Launch UI
+gr.Interface(
+    fn=generate_audio,
+    inputs=gr.Textbox(label="Enter your sound prompt"),
+    outputs=gr.Audio(type="filepath"),
+    title="Stable Audio Generator"
+).launch()